BRU_ ACKNOWLEDGEMENT McKinsey & Company would like to thank and recognize the important collaborative contributions of Kenneth Brill and The Uptime Institute.

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Presentation on theme: "BRU_ ACKNOWLEDGEMENT McKinsey & Company would like to thank and recognize the important collaborative contributions of Kenneth Brill and The Uptime Institute."— Presentation transcript:

1 BRU_ACKNOWLEDGEMENTMcKinsey & Company would like to thank and recognize the important collaborative contributions of Kenneth Brill and The Uptime Institute to the development of this report and its recommendations. The Institute provided critical insight based on their many years of experience as well as proprietary data and analysis not previously made publicCopyright McKinsey & Company

2 BRU_EXECUTIVE SUMMARYThe rapid recent (and projected) growth in the number and size of Data centers creates two significant challenges for enterprises:Data center facilities spend (CapEx and OpEx) is a large, quickly growing and very inefficient portion of the total IT budget in many technology intensive industries such as financial services and telecommunications. Some intensive data center users will face meaningfully reduced profitability if current trends continueFor many industries, data centers are one of the largest sources of Greenhouse Gas (GHG) emissions. As a group, their overall emissions are significant, in-scale with industries such as airlines. Even with immediate efficiency improvements (and adoption of new technologies) enterprises and their equipment providers will face increased scrutiny given the projected quadrupling of their data-center GHG emissions by 2020The primary drivers of poor efficiency are:Poor demand and capacity planning within and across functions (business, IT, facilities)Significant failings in asset management (6% average server utilization, 56% facility utilization)Boards, CEOs, and CFOs are not holding CIOs accountable for critical data center facilities CapEx and data center operational efficiencyImproving efficiency is the best near term means to solving the twin challenges of rising spend and GHG emissions. We propose a three part solution to double IT energy efficiency by 2012 and to arrest the growth of GHG emissions from data centers:Rapidly mature and integrate asset management capabilities to reach the same par as the Security functionMandate inclusion of true total cost of ownership (including data center facilities) in business case justification of new products and applications to throttle excess demandFormally move accountability for data center critical facilities expense and operations to the CIO and appoint internal “Energy Czars” with an operations and technology mandate to double IT energy efficiency by 2012To achieve this doubling of energy efficiency CIOs, equipment manufacturers, as well as industry groups in dialog with regulators should quickly establish automotive style “CAFE” metrics that will measure the individual and combined energy efficiency of corporate, public sector and 3rd party hosted data centers. We propose one metric here for discussion and adoption. This metric would deliver immediate financial and transparency benefits to executive management of enterprises large and small and could become a government recognized measure of efficiency

3 DATA CENTER COST IS APPROXIMATELY A QUARTER OF TODAY’S IT COSTS . . .428BRU_DATA CENTER COST IS APPROXIMATELY A QUARTER OF TODAY’S IT COSTS . . .Breakdown of average IT cash costs at a typical company, percentDevelopment20Application Development40Not all facilities within IT budgetUnrealistically long depreciation timeframes artificially hide data center costsCurrent constr-uction boom is not a one time catch-up investment. Server growth will require add’l new data center construction every 3-5 yearsMaintenance20ITEnd Users10015Network (LAN/ WAN)15Infrastructure and OperationsFacilities608Data Center25Hardware, Storage17Other5Note: Total IT budget is illustrative of a typical companySource: McKinsey analysis

4 428BRU_AND DATA CENTER IT COSTS WILL CONTINUE TO GROW AS THE NUMBER OF SERVERS HOUSED WITHIN DATA CENTERS GROWS RAPIDLY . . .Servers hosted within data centers within USACAGR reducing due to increased use of virtualizationPower consumption per server increasing even faster as newer machines consume much more powerData center spend is growing rapidly due to increased demandChina and other developing countries are projected to grow even more rapidlyGrowing data center spend is putting pressure on other IT initiatives or functions (e.g., applications development, end user computing)18,00016,00014,00013.6% CAGR9.9% CAGR12,000Installed volume servers – U10,0008,0006,0004,0002,00020002001200220032004200520062010YearNote: Total IT budget is illustrative of a typical companySource: EPA 2007 Report to congress

5 55,000BRU_SERVERS AREN’T “CHEAP” BECAUSE THEY INCUR SUBSTANTIAL FACILITY (POWER AND COOLING) COSTS OVER THEIR LIFEAnnual OpEx to support a mid-tier ($2,500) server, dollarsTrue costs are often 4-5x the cost of the server alone over a 5-10 year lifetime of a serverIT hardware energy consumption drives Facility costsServers are often housed in a higher Tier Data Center than necessary, further driving Facility costsFacility costs are growing more rapidly (20%) than overall IT spend (6%)*****Facilities Depreciation*****Facility Operations***Data center tierTier IITier IIITier IVSource: Uptime Institute

6 46BRU_HIGHER LOAD DENSITY ALSO CONTRIBUTES TO HIGHER ENERGY COSTS CURRENTLY INCREASING AT 16% PER YEARTotal data centers energy bill, $ Billions3 Drivers of 16% CAGR Energy Cost IncreaseInstalled base on server is growing by 16% and projected to grow to million servers worldwide by 2010Energy consumption per server is growing by 9% as growth in performance pushes demand for energyEnergy unit price has increased an average of 4%*****E*E*ENote: Weighted average consumption for top selling volume serversSource: IDC, “Estimating total power consumption by servers in the US and the world” from Jonathan G. Koomey, Ph.D.

7 22BRU_WITHOUT RADICAL CHANGES IN OPERATIONS, MANY COMPANIES WITH LARGE DATA CENTERS FACE REDUCED PROFITABILITYDISGUISED CLIENT EXAMPLEOpex projectionCapex projectionRapid growth in Opex due to:40% transaction volume growth16% database record volume growthTrading to continue increasing at CAGR of 15%A number of business units plan to offer new productsHigh regional demand in AsiaLarge increase in capital spend to increase depreciation expenseAdditional labor to manage growing demandIncreased facilities costs (e.g., energy)Rapid growth in Capex due to:Urgent need to meet medium term additional demand (available capacity projected to be fully consumed in next 30 months)Need to meet regulatory disaster recovery goalsSmaller data centers are out of space and have obsolete technologyInflexible configuration of the main data center does not allow expansion despite low floor densityData center cost as percent of total revenue all time highData center cost growing twice as rapidly as revenueData center construction investment significantly affects profitability for next two yearsSource: McKinsey analysis

8 178BRU_DUE TO ENORMOUS ENERGY CONSUMPTION, DATA CENTERS’ CARBON FOOTPRINT IS ALSO SURPRISINGLY HIGH AND GROWINGKey points on data centers’ greenhouse gas emissionsCarbon dioxide emissions as percentage of world total – industriesPercentData center electricity consumption is almost .5% of world production*Average data center consumes energy equivalent to 25,000 householdsWorldwide energy consumption of DC doubled between and 2006Incremental US demand for data center energy between now and 2010 is equivalent of 10 new power plants90% of companies running large data centers need to build more power and cooling in the next 30 monthsData centersAirlinesShipyardsSteel plantsCarbon emissions – countriesMt CO2 p.a.Data centersArgentinaNether- landsMalaysia* Including custom-designed servers (e.g., Google, Yahoo)Source: Financial Times; Gartner report 2007; Stanford University; AMD; Uptime Institute; McKinsey analysis

9 86.0BRU_ONGOING INITIATIVES NOT WITHSTANDING, EMISSIONS WILL QUADRUPLE BY 2020 CAUSING INTENSE SCRUTINY FROM REGULATORS, ACTIVISTS AND CORPORATE BOARDSCurrent technology focused initiatives will not be sufficient to reverse trendEmissions are set to quadruple by 2020The carbon footprint has begun to attract scrutiny and legislation (e.g., US Public Law requires EPA to submit a report on energy consumption of data centers to US congress)EPA has advocated use of separate energy meters for large data centers and development of procurement standardsThe European Union is developing a voluntary Code of Conduct for data centers proscribing energy efficiency best practices.Data center carbon footprint is expected to affect even the industries that are traditionally considered “clean” (e.g., telecom, media, technology)Due to higher performance per m2, the electricity consumption will grow faster than the number of serversEmission from data centers will surpass those from many industry such as AirlinesEPA driven initiative to reduce power consumption at homes, commercial buildings, and electronicsGlobal consortium to reduce energy consumptions of data centersEmissions from Data Centers worldwideMt CO2670Third party hosting service provider based at Cheyenne, WY powered 100% by wind power*Renewable Fuels Association is a trade group of US ethanol industry that promotes policies, research, and regular to increase use of ethanol as fuel170**Source: IDC U.S. and Worldwide Server Installed Base Forecast; McKinsey analysis

10 BRU_EXECUTIVE SUMMARYThe rapid recent (and projected) growth in the number and size of Data centers creates two significant challenges for enterprises:Data center facilities spend (CapEx and OpEx) is a large, quickly growing and very inefficient portion of the total IT budget in many technology intensive industries such as financial services and telecommunications. Some intensive data center users will face meaningfully reduced profitability if current trends continueFor many industries, data centers are one of the largest sources of Greenhouse Gas (GHG) emissions. As a group, their overall emissions are significant, in-scale with industries such as airlines. Even with immediate efficiency improvements (and adoption of new technologies) enterprises and their equipment providers will face increased scrutiny given the projected quadrupling of their data-center GHG emissions by 2020The primary drivers of poor efficiency are:Poor demand and capacity planning within and across functions (business, IT, facilities)Significant failings in asset management (6% average server utilization, 56% facility utilization)Boards, CEOs, and CFOs are not holding CIOs accountable for critical data center facilities CapEx and data center operational efficiencyImproving efficiency is the best near term means to solving the twin challenges of rising spend and GHG emissions. We propose a three part solution to double IT energy efficiency by 2012 and to arrest the growth of GHG emissions from data centers:Rapidly mature and integrate asset management capabilities to reach the same par as the Security functionMandate inclusion of true total cost of ownership (including data center facilities) in business case justification of new products and applications to throttle excess demandFormally move accountability for data center critical facilities expense and operations to the CIO and appoint internal “Energy Czars” with an operations and technology mandate to double IT energy efficiency by 2012To achieve this doubling of energy efficiency CIOs, equipment manufacturers, as well as industry groups in dialog with regulators should quickly establish automotive style “CAFE” metrics that will measure the individual and combined energy efficiency of corporate, public sector and 3rd party hosted data centers. We propose one metric here for discussion and adoption. This metric would deliver immediate financial and transparency benefits to executive management of enterprises large and small and could become a government recognized measure of efficiency

11 BRU_DESPITE RAPIDLY GROWING COSTS, DATA CENTERS ARE OPERATIONALLY VERY INEFFICIENT AND UNDERUTILIZEDDISGUISED CLIENT EXAMPLEUPS, cooling, and other facilities are consistently underutilized . . .Server utilization remains very low. . .100100909080807070606050504040About one third of all sites are less than 50% utilized, average is 55%Little co-relation between size and capacity utilization30Up to 30% servers are dead3020201010102030405090100Average daily utilization (percent)Installed capacity, KWA small number of organizations are starting to monitor server utilization, however very few organizations monitor facilities energy efficiency or utilization* Sample size – 45 data centersSource: Uptime Institute

13 46BRU_1. DECISIONS ABOUT APPLICATIONS AND INFRASTRUCTURE DO NOT ADEQUATELY CONSIDER THEIR IMPACT ON DC OPERATIONS AND COSTTrue Application TCO PercentTrue Infrastructure TCO PercentNot considered in TCO business case for ‘go/no-go’ decisionILLUSTRATIVEApplication development – labor/licensesHardware cost (Opex)Software (Opex)Maintenance and supportLimited understanding of data center TCO and limited access to relevant dataLimited understanding of choices that can influence data center costNo representation of data center in design, planning, and approval process for new applications and hardware componentsMaintenance (labor and parts)Servers, network, and other hardwareNetwork andconnectivityData center utilizationData centerutilization (facilities, DR)Total cost of applicationTotal cost of infrastructureExamples of poor applicationdecisions…Applications that don’t reduce usage of monitors during off peak/closed hoursLimited use of grid computingComputation load is not shifted among systems to maximize energy usedExamples of poor infrastructure decisions…Storage usage not maximizedLimited use of MAID (massive array of idle disks)Poor layout designEquipment that is physically largeSource: Uptime Institute; EPA report; McKinsey analysis

15 BRU_3. LACK OF CIO/BOARD OVERSIGHT DURING TYPICAL CAPEX APPROVAL PROCESS FOR DATA CENTERS OFTEN RESULTS IN A SIGNIFICANT OVERSPENDTypical CapEx approval process for data centersReview and approvalImplementationRequirementsDesignNo active decommissioning to free up existing facility capacityAssumes highest case demand projectionsPoor demand forecastingAlternate source of supply (e.g., third party hosting facility) not consideredGold plating to “future proof” data center capacityLimited use of future modular expansion capacityLack of understanding or priority of IT and facility design choices that can significantly lower power requirementsIT utilization data and demand projections are seldom challengedUnitary IT solutions as “fact accompli” assumptions and trade offs are difficult to validateCXOs and boards often are not suffic-iently knowledge-able to challenge assumptions or require alternative economic choicesItems often missed in design phase (e.g., migration costs create project overruns)Specialized project management and cross functional oversight skills often are lacking resulting in delays and cost over runsSource: McKinsey analysis

16 BRU_4. MOST DATA CENTER FACILITIES DO NOT FULLY USE ENERGY EFFICIENT DESIGNSAMPLE CHALLENGES OBSERVEDTemperatures in the cold aisle are much colder than required and can be increased to 74°. Similarly, the hot aisle should be hot (90° or even higher)High density air cooling usually increases total facility CapEx for electrical and mechanical capacity as well as total energy consumption. Water cooling saves energy and is simpler and more reliableAll UPS modules, chillers, cooling units, etc. are installed initially instead of waiting until the center is more fully occupiedEfficiency focus is on 80% or higher loads instead of the 10-30% loads where most facilities operate for much of their livesWinter free-cooling opportunities worth hundreds of thousands of dollars annually are not used because office building piping designs were used erroneously.Source: Uptime Institute

17 BRU_EXECUTIVE SUMMARYThe rapid recent (and projected) growth in the number and size of Data centers creates two significant challenges for enterprises:Data center facilities spend (CapEx and OpEx) is a large, quickly growing and very inefficient portion of the total IT budget in many technology intensive industries such as financial services and telecommunications. Some intensive data center users will face meaningfully reduced profitability if current trends continueFor many industries, data centers are one of the largest sources of Greenhouse Gas (GHG) emissions. As a group, their overall emissions are significant, in-scale with industries such as airlines. Even with immediate efficiency improvements (and adoption of new technologies) enterprises and their equipment providers will face increased scrutiny given the projected quadrupling of their data-center GHG emissions by 2020The primary drivers of poor efficiency are:Poor demand and capacity planning within and across functions (business, IT, facilities)Significant failings in asset management (6% average server utilization, 56% facility utilization)Boards, CEOs, and CFOs are not holding CIOs accountable for critical data center facilities CapEx and data center operational efficiencyImproving efficiency is the best near term means to solving the twin challenges of rising spend and GHG emissions. We propose a three part solution to double IT energy efficiency by 2012 and to arrest the growth of GHG emissions from data centers:Rapidly mature and integrate asset management capabilities to reach the same par as the Security functionMandate inclusion of true total cost of ownership (including data center facilities) in business case justification of new products and applications to throttle excess demandFormally move accountability for data center critical facilities expense and operations to the CIO and appoint internal “Energy Czars” with an operations and technology mandate to double IT energy efficiency by 2012To achieve this doubling of energy efficiency CIOs, equipment manufacturers, as well as industry groups in dialog with regulators should quickly establish automotive style “CAFE” metrics that will measure the individual and combined energy efficiency of corporate, public sector and 3rd party hosted data centers. We propose one metric here for discussion and adoption. This metric would deliver immediate financial and transparency benefits to executive management of enterprises large and small and could become a government recognized measure of efficiency

18 WE PROPOSE A THREE PART SOLUTION TO IMPROVING DATA CENTER EFFICIENCYBRU_WE PROPOSE A THREE PART SOLUTION TO IMPROVING DATA CENTER EFFICIENCY12Improve IT asset management capabilitiesImprove IT demand forecasting capabilitiesPromote regular dialog between business, IT, and FacilitiesUse new technology to increase server utilizationOptimize current facilities utilization with a view on power costEnsure that solutions are not over-designedInclude energy efficiency as an important criteria in hardware procurementImplement facilities best practicesUse true total cost of ownership (TCO) of a data center by incorporating facilities costCompute TCO over entire life span of data centerIncrease transparency of data center costsInclude data center TCO in application and infrastructure decisionsDevelop ability to manage true cost of IT ownershipDevelop mature IT asset managementEstablish an integrated plan including energy efficiency3Develop an integrated plan, measurable goals and timeline to double data center efficiencyMove accountability for facilities expense (CapEx and OpEx) and facility operations to the CIOAppoint internal “Energy Czars” with a mandate to improve data center efficiency while maintaining business availability and reliability needsImplement chargeback for existing appsImprove large CapEx approval process for data centersPublicly commit to green house gas reduction targetsSource: McKinsey analysis

19 DEVELOP MATURE ASSET MANAGEMENT AND IT PRODUCTIVITY CAPABILITIESImproved asset managementBRU_DEVELOP MATURE ASSET MANAGEMENT AND IT PRODUCTIVITY CAPABILITIES-Demand managementEnsure technical input from solution architect during RFP/RFI processAggregate pipeline forecasts with solution architect and data center operationsUse stage gate approach to qualify likelihood of demandConfiguration/ locationBuild larger shells (or campuses of shells) by dividing floor space into smaller logical units (“fields”) that are engineered to specific workloads and built without major M&E interruptionsOptimize current location portfolio with a view of operational and energy spendLayout/ Cabinet allocationsRationalize cabinet allocation by eliminate/combine cabinets with few assets and discouraging allocations of space by whole cabinet to business units/ LOBsVerify that allocated cabinets are used, don’t report allocate cabinet as used automaticallyUtilize ITIL configuration mgt to track asset utilization/chargeback/de-commissioningDensityReduce role of support infrastructure (routers/SANS) to contain density requirementsOptimize rack utilization by eliminating unnecessary peripherals and fully loading each rackUtilizationVirtualize/stack to reduce the number of physical servers; increase rack utilizationKill comatose servers and storage as up to 30% of server may be “dead”Enable hardware power save featuresEliminate network port redundancySourcingMaintain internal control on most critical systems and co-locate less critical servicesMove non critical system to managed provider in a virtualized environment with expectation to move more as the services mature and establish better track record for reliabilityInclude energy efficiency as an important criteria in hardware procurementFacility operationsMeasure and report energy efficiencyOptimize cooling unit set-points, balance number of cooling units running, number, and location of perforated tiles with actual loadOptimize mechanical plant operation, raise chilled water supply temperature, eliminate “dueling” cooling units, utilize “free-cooling” opportunities, monitor humidification/dehumidification energySeal cable openings and install blanking platesSource: McKinsey analysis

20 ENHANCE DEMAND FORECASTING CAPABILITIESImproved asset managementBRU_ENHANCE DEMAND FORECASTING CAPABILITIESBest practicesDescriptionImprove forecast accuracyTrack variation in forecast accuracy, incentivising business and IT to minimise deviationsUse stage gate approach to qualify likelihood of demandUse tools and processes to capture and collate commandValue at stake from effective demand forecasting15-25% reduction in overall operational costs by avoiding overbuildsDelayed construction of incremental power and cooling capacity reduces CapExBuild dynamic demand modelsIncorporate drivers to account for organic growth, unplanned business events and business cyclesUse scenario models to understand how different potential scenarios drive data center capacityInvolve solutions architectsEnsure technical input from architects during design processEnsure data center representation in projects approval processDesign Applications and hardware to optimize computingAggressively pursue demand reductionConsider various ways to reduce data center space and power demands, from application and infrastructure sizing through to floor optimization.Instill culture of treating data center capacity as a scarce and expensive asset rather than as a bathtub to be filledEstablish business-technology dialogEnsure Technology teams present clear options trading off between key business drivers and underlying costs e.g., true cost of increments of availability, opportunity to acquire less floor space if businesses adopt wholesale virtualization, etc.Draw economic connection between business demand and true TCODevelop analytic approach for connecting business demand to application requirements, application requirements to infrastructure requirements and infrastructure requirements to data center requirementsSource: McKinsey analysis

24 35Improved asset managementBRU_USE VIRTUALIZATION TECHNOLOGY TO IMPROVE UTILIZATION AND REDUCE NUMBER OF PHYSICAL SERVERSILLUSTRATIVEAverage utilization by hour, percentAverage utilization by hourAverage utilization is very low even during peak hours in non virtualized environmentPhysical servers count100Time, hours24h*Utilization in virtualized environmentConsolidate servers by stacking and virtualizing to increase average utilization1001224hUtilization during special periodsConsider weekly, seasonal and other (e.g., year-end) variation in utilization100**1224hConsider power saving for non-production hardwareSource: McKinsey analysis

25 True cost of ownership viewBRU_OVER ENTIRE LIFE SPAN, DATA CENTER IS A SIGNIFICANT COMPONENT OF IT COST AND SHOULD BE INCLUDED IN APP/INFRA DECISIONSData center cost is negligible during application concept/design and development phase. Hence is often ignoredData center cost becomes significant and poor choices at design stage further increase this costTotal costData center costsAnalysis/ laborSupportSupportLaborInternallaborLicenseSupportTrainingData centerData centerData centerData centerInsta-llationStable lifeEnd of lifeConcept/ designDevelopmentApplication life cycleSource: McKinsey analysis

26 True cost of ownership viewBRU_INDEPENDENT ARCHITECTURE AND STANDARDS COUNCIL REVIEWS ENSURE THAT IT SOLUTIONS ARE NOT OVERDESIGNEDPre-productionUnnecessary software is not includedSoftware are architecture is sized for effect and scaleHardware is correctly sizedSoftware complies to all existing standardsHardware life cycle is clearly markedLife cycle costs of hardware and facilities are included in business casesProductionProduction clearance is signed before moving to production environmentStandards are maintained throughout the life of given hardware and softwareApplications and associated software are upgraded to ensure consistencyAs many applications moved to shared environment as possiblePost-productionSoftware and hardware are decommissioned by due dateAll associated systems are decommissionedAll data moved to tapes and shared storage! Purge any data not needed anymoreReuse rack space and IT kW capacitySource: McKinsey analysis

27 DEVELOP AN INTEGRATED PLAN TO DOUBLE DATA CENTER EFFICIENCY BY 2012Establish an integrated plan including energy efficiencyBRU_DEVELOP AN INTEGRATED PLAN TO DOUBLE DATA CENTER EFFICIENCY BY 2012SummaryRationaleBenefitsMove full DC operations respo-nsibility to CIOCentralize accountability for spend and performanceCurrently accountability is divided between facilities/corporate real state and IT which distorts total cost viewAllows CIO to make rational decisions on facilitiesBrings all DC cost under a single standard reportingEnsures single point responsibilityAppoint “energy czars”Integrate and prioritize energy-efficiency measuresEnergy is “nobody’s” business todayLack of awareness about the design choices to optimize energy usageInclude energy consumption and facility costs as a key criteria for IT project ROI analysis and decision makingBring accountability ?Double energy efficiency by 2012Quickest and easiest way to improve return on assets and reduce GHG emissionsProcess improvement and current technology can drive energy efficiency significantly higherSets clear directions for the companySignificantly lowers costPublicly commit to emission targetsRaise commitment to and profile of targets within organizationMany companies can reduce GHG emissions without adversely affected their day-to-day businessProactive addresses a political issue which otherwise might be mandated by regulators, boards, or NGOsSource: McKinsey analysis

28 Establish an integrated plan including energy efficiencyBRU_MAKE CIO ACCOUNTABLE FOR EFFICIENCY OF DATA CENTERS AND DOUBLING EFFICIENCY BY 2012Siloed organizationsFacilities and IT teams have limited interactions when designing or efficiently operating data centers leading to multiple layers of conservatism and waste. There is little cross-functional learning and coordinationExecutive decision makers are not provided with sufficient facility economic outcomes and alternatives resulting from IT application investment decisionsLimited transparencyFacilities have intelligence on IT power consumption, but no insight into how IT equipment being utilized, how efficiently power within IT hardware is being utilized, nor what the future is. This leads to over provisioningThe data center electrical bill is likely to be included within a larger electrical bill and the bill typically does not go to ITTools for modeling IT electrical consumption are not widely available and are not commonly used during data center designMisaligned metricsFacility costs (both OpEx and CapEx) not clearly linked to any particular IT application decision nor IT operating practices. They are therefore viewed as inevitableFew, if any, metrics link facilities and corporate real estate groups with IT/CIO efficiency metricsSource: APC “Implementing energy efficient data centers”

31 BRU_EXECUTIVE SUMMARYThe rapid recent (and projected) growth in the number and size of Data centers creates two significant challenges for enterprises:Data center facilities spend (CapEx and OpEx) is a large, quickly growing and very inefficient portion of the total IT budget in many technology intensive industries such as financial services and telecommunications. Some intensive data center users will face meaningfully reduced profitability if current trends continueFor many industries, data centers are one of the largest sources of Greenhouse Gas (GHG) emissions. As a group, their overall emissions are significant, in-scale with industries such as airlines. Even with immediate efficiency improvements (and adoption of new technologies) enterprises and their equipment providers will face increased scrutiny given the projected quadrupling of their data-center GHG emissions by 2020The primary drivers of poor efficiency are:Poor demand and capacity planning within and across functions (business, IT, facilities)Significant failings in asset management (6% average server utilization, 56% facility utilization)Boards, CEOs, and CFOs are not holding CIOs accountable for critical data center facilities CapEx and data center operational efficiencyImproving efficiency is the best near term means to solving the twin challenges of rising spend and GHG emissions. We propose a three part solution to double IT energy efficiency by 2012 and to arrest the growth of GHG emissions from data centers:Rapidly mature and integrate asset management capabilities to reach the same par as the Security functionMandate inclusion of true total cost of ownership (including data center facilities) in business case justification of new products and applications to throttle excess demandFormally move accountability for data center critical facilities expense and operations to the CIO and appoint internal “Energy Czars” with an operations and technology mandate to double IT energy efficiency by 2012To achieve this doubling of energy efficiency CIOs, equipment manufacturers, as well as industry groups in dialog with regulators should quickly establish automotive style “CAFE” metrics that will measure the individual and combined energy efficiency of corporate, public sector and 3rd party hosted data centers. We propose one metric here for discussion and adoption. This metric would deliver immediate financial and transparency benefits to executive management of enterprises large and small and could become a government recognized measure of efficiency

32 BRU_CORPORATE AVERAGE DATA EFFICIENCY (CADE) V1.0 MEASURES DATA CENTER EFFICIENCY ACROSS THE CORPORATE FOOTPRINTFor Version 2.0CADE measures across the enterprise footprintEach data center is measured independentlyA average value is determined by weighting data centers based upon installed facility capacityCADE can be used in conjunction with each DC’s energy source(s) to determine “cleanliness” of GHG emissionsCADE=FACILITY EFFICIENCYxIT ASSET EFFICIENCYFacility Energy Efficiency%Facility Utilization %ITUtilization %IT Energy Efficiency%xxEnergy efficiency is actual IT Load divided by total power consumed by the data centerUtilization is the actual IT Load (servers, storage, network equipment) actually used divided by Facility CapacityServer utilization is the average CPU utilization (not MIPS weighted, etc.)Future energy efficiency metric for servers/ midrange/ mainframe, storage, network. etc.Note: CADE, Version 2.0 is expected to include storage and networking measurements additionallySource: McKinsey analysis

33 BRU_WE PROPOSE FIVE CADE TIERING LEVELS TO ALLOW EASIER COMPARISONS LEVELS AND TO SET TARGETS FOR IMPROVEMENTExpected actual range for many data centers todayCADE TierRangeLevel 10-5%Expected range for most data centers to target by 2012CADE tiering will set efficiency targets for data center management (e.g., increase from CADE tier 2 to 3 in 18 months)CADE ranges will flex over time as companies begin standardizing on its measurementAdditional updating when server, storage and networking energy efficiency are included in the measurementLevel 25-10%Level 310-20%Level 420-40%Level 5>40%Source: McKinsey analysis